SVM

Before moving forward with the to-do list, let’s throw a Random Forest to it.

SVM

For many reasons, Random Forest is usually a very good baseline model. In this particular case I started with the polynomial OLS as baseline model, just because it was so evident from the correlations that the relationship between temperature and consumption follows a polynomial shape. But let’s go back to a beloved RF.

/home/runner/work/strom/strom/.venv/lib/python3.12/site-packages/sklearn/svm/_classes.py:31: FutureWarning:

The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.

/home/runner/work/strom/strom/.venv/lib/python3.12/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning:

Liblinear failed to converge, increase the number of iterations.

Model Cards provide a framework for transparent, responsible reporting. 
 Use the vetiver `.qmd` Quarto template as a place to start, 
 with vetiver.model_card()
Writing pin:
Name: 'wd-svm'
Version: 20250410T160943Z-1b835

♻️  stepit 'svm_raw': is up-to-date. Using cached result for `strom.modelling.assess_model()` 2025-04-10 16:09:43

	Single Split		CV
	train	test	test	train
MAE - Mean Absolute Error	2.759698	2.489427	3.087172	3.141476
MSE - Mean Squared Error	17.777598	18.100418	15.989639	21.179925
RMSE - Root Mean Squared Error	4.216349	4.254459	3.781907	4.580536
R2 - Coefficient of Determination	0.815390	0.801454	-7.118546	0.784520
MAPE - Mean Absolute Percentage Error	0.311556	0.275527	0.660358	0.254285
EVS - Explained Variance Score	0.818426	0.803021	-1.855572	0.824080
MeAE - Median Absolute Error	2.164015	1.688725	2.542521	2.327501
D2 - D2 Absolute Error Score	0.612945	0.648707	-1.968504	0.553734
Pinball - Mean Pinball Loss	1.379849	1.244713	1.543586	1.570738

Scatter plot matrix

Test
Train

Observed vs. Predicted and Residuals vs. Predicted

Check for …

check the residuals to assess the goodness of fit.

white noise or is there a pattern?
heteroscedasticity?
non-linearity?

Test
Train

Normality of Residuals:

Check for …

Are residuals normally distributed?

Test
Train

Test
Train

Well, not that bad, but it is overfitting quite a lot.

♻️  stepit 'grid_search_pipe': is up-to-date. Using cached result for `strom.modelling.grid_search_pipe()` 2025-04-10 16:09:47
Model Cards provide a framework for transparent, responsible reporting. 
 Use the vetiver `.qmd` Quarto template as a place to start, 
 with vetiver.model_card()
Writing pin:
Name: 'wd-svm'
Version: 20250410T160947Z-46ac4

♻️  stepit 'svm_tuned': is up-to-date. Using cached result for `strom.modelling.assess_model()` 2025-04-10 16:09:47

Metrics

Plot
Table

	Single Split		CV
	train	test	test	train
MAE - Mean Absolute Error	2.341431	2.247375	2.147532	2.581758
MSE - Mean Squared Error	15.653589	16.779396	7.877316	17.898406
RMSE - Root Mean Squared Error	3.956462	4.096266	2.711544	4.228775
R2 - Coefficient of Determination	0.837447	0.815945	-1.935593	0.817129
MAPE - Mean Absolute Percentage Error	0.184828	0.171403	0.474445	0.178073
EVS - Explained Variance Score	0.839705	0.841197	-1.089626	0.820615
MeAE - Median Absolute Error	1.497370	1.414702	1.790379	1.581437
D2 - D2 Absolute Error Score	0.671608	0.682863	-0.894147	0.631738
Pinball - Mean Pinball Loss	1.170716	1.123688	1.073766	1.290879